Semantics or functionality preservation means that the synthetic samples can still execute the original malicious function intended by the attacker. Various researchers have employed GANs to synthesise the network traffic data to address the issue of data imbalance, and adversarial evasion attacks [1]}, [2]}, [3]}. However, GANs are not good at generating categorical features [4]}. Researchers have used one-hot representation [5]}, or IP2Vec techniques [6]}, [7]} to transform IP addresses into integer values to input to GANs for data generation. Since GANs follow the probability distribution of the input data so there is a high chance that the synthetic samples may lie outside the semantic limits. The perturbations could be easily perceived as anomalies by the ML detectors. To preserve the functionality, the perturbation j* in Equation REF  should be small enough not to be perceived by the detectors as malicious and distant enough to be considered normal samples. At the same time, the malicious activity must not be compromised. For this reason, several researchers have proposed functionality preservation by modifying only the non-functionality preserving features using GANs [5]}, [4]}, [10]}. However, various research works claim to preserve the malicious functionality of the generated samples using deep reinforcement learning.
